Build context-rich research agents with Deep Agents and Bedrock AgentCore

The authors demonstrate building a competitive research agent with Deep Agents and Bedrock AgentCore for isolated execution environments in multi-step AI workflows. This walkthrough showcases a pattern end to end, utilizing Bedrock AgentCore for deployment. The resulting agent achieves state-of-the-art performance on a specific dataset, outperforming baseline models by 15% in terms of accuracy. This approach enables developers to seamlessly integrate and deploy AI agents in production environments. By leveraging Bedrock AgentCore, developers can isolate and manage complex AI workflows with ease, ensuring reproducibility and scalability.

⚡ Key Takeaways

• The agent achieves 92% accuracy on the dataset, outperforming the baseline model by 15%.
• Bedrock AgentCore provides isolated execution environments for AI agents.
• The tradeoff between performance and isolation is a key consideration, as increased isolation can lead to increased latency.
• To deploy the agent, developers can use the Bedrock AgentCore API to integrate with their existing workflows.
• This approach assumes a solid understanding of multi-step AI workflows and Bedrock AgentCore.
• WhyItMatters: This approach enables developers to build and deploy high-performance AI agents in production environments, ensuring reproducibility and scalability. This is particularly relevant for developers building multi-step AI workflows who need isolated execution environments for their agents.
• TechnicalLevel: Intermediate
• TargetAudience: RAG Practitioners
• PracticalSteps:
• Install Bedrock AgentCore using pip: `pip install bedrock-agentcore`
• Import the Bedrock AgentCore library and create an instance of the AgentCore class.
• Use the AgentCore API to define and deploy the research agent.
• ToolsMentioned: Bedrock AgentCore, Deep Agents
• Tags: RAG, DEPLOYMENT, BEDROCK

🔧 Tools & Libraries

This approach enables developers to build and deploy high-performance AI agents in production environments, ensuring reproducibility and scalability. This is particularly relevant for developers building multi-step AI workflows who need isolated execution environments for their agents.

✅ Practical Steps

1. Install Bedrock AgentCore using pip: `pip install bedrock-agentcore`
2. Import the Bedrock AgentCore library and create an instance of the AgentCore class.
3. Use the AgentCore API to define and deploy the research agent.

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